Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -1070.34 +/- 1311.70
+      value: 262.24 +/- 21.35
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x111ef7100>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x111ef71a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x111ef7240>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x111ef72e0>", "_build": "<function ActorCriticPolicy._build at 0x111ef7380>", "forward": "<function ActorCriticPolicy.forward at 0x111ef7420>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x111ef74c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x111ef7560>", "_predict": "<function ActorCriticPolicy._predict at 0x111ef7600>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x111ef76a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x111ef7740>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x111ef77e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x111eee740>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 10048, "_total_timesteps": 10000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1753041271084144000, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAADmswC+llPuPzIs+L5DxhM/zOK7PoEaHD4AAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0047999999999999154, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1570, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-1.5       -1.5       -5.        -5.        -3.1415927 -5.\n -0.        -0.       ]", "high": "[1.5       1.5       5.        5.        3.1415927 5.        1.\n 1.       ]", "low_repr": "[-1.5       -1.5       -5.        -5.        -3.1415927 -5.\n -0.        -0.       ]", "high_repr": "[1.5       1.5       5.        5.        3.1415927 5.        1.\n 1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "n_steps": 64, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "macOS-16.0-arm64-arm-64bit Darwin Kernel Version 25.0.0: Tue Jun 17 00:08:06 PDT 2025; root:xnu-12377.0.122.0.1~120/RELEASE_ARM64_T6041", "Python": "3.12.7", "Stable-Baselines3": "2.1.0", "PyTorch": "2.7.1", "GPU Enabled": "False", "Numpy": "2.3.1", "Cloudpickle": "3.1.1", "Gymnasium": "0.29.1"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x1283cc220>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x1283cc2c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x1283cc360>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x1283cc400>", "_build": "<function ActorCriticPolicy._build at 0x1283cc4a0>", "forward": "<function ActorCriticPolicy.forward at 0x1283cc540>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x1283cc5e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x1283cc680>", "_predict": "<function ActorCriticPolicy._predict at 0x1283cc720>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x1283cc7c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x1283cc860>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x1283cc900>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x1281cb540>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1753041399803105000, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.004885333333333408, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 368, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-1.5       -1.5       -5.        -5.        -3.1415927 -5.\n -0.        -0.       ]", "high": "[1.5       1.5       5.        5.        3.1415927 5.        1.\n 1.       ]", "low_repr": "[-1.5       -1.5       -5.        -5.        -3.1415927 -5.\n -0.        -0.       ]", "high_repr": "[1.5       1.5       5.        5.        3.1415927 5.        1.\n 1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVBAMAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjGAvVXNlcnMvbHVpamFpdC9kZWVwcmwtY291cnNlL3ZlbnYvbGliL3B5dGhvbjMuMTIvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5RLg0MI+IAA2A8SiAqUQwCUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxgL1VzZXJzL2x1aWphaXQvZGVlcHJsLWNvdXJzZS92ZW52L2xpYi9weXRob24zLjEyL3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUaACMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGghfZR9lChoGIwEZnVuY5SMDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgZjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "macOS-16.0-arm64-arm-64bit Darwin Kernel Version 25.0.0: Tue Jun 17 00:08:06 PDT 2025; root:xnu-12377.0.122.0.1~120/RELEASE_ARM64_T6041", "Python": "3.12.7", "Stable-Baselines3": "2.1.0", "PyTorch": "2.7.1", "GPU Enabled": "False", "Numpy": "2.3.1", "Cloudpickle": "3.1.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.26.2"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:892b1aecc14eec0589fdb6c3d98923ccb551e20ccee3140c27ef026c913a82c8
-size 147793
+oid sha256:de6c6fd40f40cbafd4ace353ebdfb0d3281471f3be8783f947c6570c95dc28ff
+size 148796

ppo-LunarLander-v2/data

@@ -4,54 +4,54 @@
         ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x111ef7100>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x111ef71a0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x111ef7240>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x111ef72e0>",
-        "_build": "<function ActorCriticPolicy._build at 0x111ef7380>",
-        "forward": "<function ActorCriticPolicy.forward at 0x111ef7420>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x111ef74c0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x111ef7560>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x111ef7600>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x111ef76a0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x111ef7740>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x111ef77e0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x1283cc220>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x1283cc2c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x1283cc360>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x1283cc400>",
+        "_build": "<function ActorCriticPolicy._build at 0x1283cc4a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x1283cc540>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x1283cc5e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x1283cc680>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x1283cc720>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x1283cc7c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x1283cc860>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x1283cc900>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x111eee740>"
+        "_abc_impl": "<_abc._abc_data object at 0x1281cb540>"
     },
-    "verbose": 0,
+    "verbose": 1,
     "policy_kwargs": {},
-    "num_timesteps": 10048,
-    "_total_timesteps": 10000,
+    "num_timesteps": 1507328,
+    "_total_timesteps": 1500000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1753041271084144000,
+    "start_time": 1753041399803105000,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAADmswC+llPuPzIs+L5DxhM/zOK7PoEaHD4AAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="
+        ":serialized:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="
     },
     "_last_original_obs": null,
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.0047999999999999154,
+    "_current_progress_remaining": -0.004885333333333408,
     "_stats_window_size": 100,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "gAWVSgsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHwEAhMB6rvLKMAWyUS1qMAXSURz/jVXvH93r2dX2UKGgGR8Bcmfs/pt78aAdLYmgIRz/kLFOwgTysdX2UKGgGR8BYTbcTJyQxaAdLUWgIRz/lMjeKsMiKdX2UKGgGR8BliXI8yN4raAdLS2gIRz/l23Sa3I+4dX2UKGgGR8BbSNEsrd30aAdLQ2gIRz/mfB3zMA3ldX2UKGgGR8BfMuPzWf9QaAdLPmgIRz/nEYfnwG4adX2UKGgGR8BwOPlbNbC8aAdLcGgIRz/oO32EkB0ZdX2UKGgGR8B4hwlIEr5JaAdLZGgIRz/pBkZrHlwMdX2UKGgGR8BRI/GyX2M9aAdLTmgIRz/psMI/qxC6dX2UKGgGR8Bp0pFb3XZoaAdLWmgIRz/qvbfxc3VDdX2UKGgGR8B2h5Jg9eQdaAdLWmgIRz/reyzHCGeudX2UKGgGR8BT4liay8jBaAdLS2gIRz/sKw6hg3LndX2UKGgGR8Byz0qur6tUaAdLY2gIRz/tRnnMdLg5dX2UKGgGR8BdusCT2WY4aAdLO2gIRz/t2Nm16Vt5dX2UKGgGR8BwaPVsk6cRaAdLWWgIRz/unG0eEIw/dX2UKGgGR8B0bO/pMYdiaAdLTmgIRz/vT6rNnoPkdX2UKGgGR8Brs+nl4keIaAdLSmgIRz/wJ4jbBXS0dX2UKGgGR8BZgwqiGnGbaAdLa2gIRz/wlAAyVObidX2UKGgGR8Bn+BOi35N5aAdLQ2gIRz/w5TAFgUlBdX2UKGgGR8B1xcDklu3uaAdLaGgIRz/xc1O0svqUdX2UKGgGR8BUL5lFtsN2aAdLPmgIRz/xv5gw482adX2UKGgGR8BaCKKLsKLLaAdLWGgIRz/yHw9aEBbOdX2UKGgGR8CBOlXgccU/aAdLfGgIRz/yvH5rP+n7dX2UKGgGR8Bwt+Jgssg/aAdLbmgIRz/zUvK2a2F4dX2UKGgGR8BgJsHyEtdzaAdLXWgIRz/ztD2Jzkp7dX2UKGgGR8BV66h11W8zaAdLRWgIRz/0LeyiVSn+dX2UKGgGR8BePSZBsyi3aAdLamgIRz/0leBxxT86dX2UKGgGR8BRVFGPPszEaAdLWWgIRz/1HizcAR02dX2UKGgGR8BrEEMLF4s3aAdLaWgIRz/1iHM2WIGhdX2UKGgGR8Bxcr+qBEroaAdLZGgIRz/2Gl/H5rP/dX2UKGgGR8Bxd4UxmCiAaAdLe2gIRz/2wmzByjpLdX2UKGgGR8BTtjSCvovBaAdLhGgIRz/3bBTGYKIBdX2UKGgGR8BmJDdznzQNaAdLemgIRz/4DgIhQm/ndX2UKGgGR8BqcSwOe8PGaAdLeGgIRz/4rQLNOdoWdX2UKGgGR8BfI7a7EpAlaAdLPWgIRz/40OZssQNDdX2UKGgGR8BWk6hxo7FLaAdLUGgIRz/5UpmVZ9uxdX2UKGgGR8B4ad9Wp6yCaAdLbmgIRz/5wnMMZxaQdX2UKGgGR8Bwytlar3j/aAdLV2gIRz/6TzI3irDJdX2UKGgGR8B+d/FglWwNaAdLnWgIRz/7FKTSsr/bdX2UKGgGR8CAI/xvNu+AaAdLWmgIRz/7n7HhjvuxdX2UKGgGR8B58uOdXko4aAdLkWgIRz/8V5fMOf/WdX2UKGgGR8B4sgFnqVyFaAdLUGgIRz/8tDQZ4wAVdX2UKGgGR8B/tTH1e0HAaAdLd2gIRz/9Vq33Hq/udX2UKGgGR8Brm2cUdq+KaAdLWWgIRz/9uuJUHY6GdX2UKGgGR8B4p/YZl4C7aAdLa2gIRz/+U8zQ/oq1dX2UKGgGR8Bx3eJP69CeaAdLaWgIRz/+7FKkEcKgdX2UKGgGR8B0hGi7CiyqaAdLW2gIRz//UOuq3mV8dX2UKGgGR8BYQoAfdRBNaAdLVGgIRz//tWhh6SkkdX2UKGgGR8B4AGFev6j4aAdLVmgIR0AAHtx+8XendX2UKGgGR8CAhB/RVp9JaAdLfGgIR0AAcxubZvkzdX2UKGgGR8B26ErlNlAeaAdLW2gIR0AApokAxSHedX2UKGgGR8CCklB1s+FDaAdLf2gIR0AA+rKeTV2BdX2UKGgGR8B3CSJO32EkaAdLW2gIR0ABQmVqveP8dX2UKGgGR8Bxvi4gA6uGaAdLXGgIR0ABdVzZHuqndX2UKGgGR8BzhEORT0g9aAdLdmgIR0ABx9JBgNPQdX2UKGgGR8B4uao60Y0maAdLVGgIR0AB+FtbcGkfdX2UKGgGR8B93lzq8lHCaAdLbWgIR0ACRkqc3EQ5dX2UKGgGR8B4AMQd0aIfaAdLXWgIR0ACeDSPU8V6dX2UKGgGR8B7vYdZJTVEaAdLcmgIR0ACyWX1J17qdX2UKGgGR8BzyKJzkp7UaAdLX2gIR0ADEbBGhEjPdX2UKGgGR8B9mvA1vVEvaAdLdGgIR0ADTcdo371qdX2UKGgGR8B6WAKohpxnaAdLWmgIR0ADk+/xlQMydX2UKGgGR8CAXR/YJ3PiaAdLi2gIR0AD8iGFi8WcdX2UKGgGR8B6wo78vVVhaAdLiWgIR0AESjk+5e7ddX2UKGgGR8BOcPNVzZHvaAdLkmgIR0AEp/I8yN4rdX2UKGgGR8B67ooy9EkTaAdLh2gIR0AFHDP4VRDUdX2UKGgGR8BuLPGIbfgraAdLo2gIR0AFg/u9eyAydX2UKGgGR8B5Sh44ZMtcaAdLTWgIR0AFsEV32VVxdX2UKGgGR8CFXKI/qxC6aAdLfmgIR0AGA97ngYP5dX2UKGgGR8B83pnIyTIOaAdLb2gIR0AGVKXfIjnndX2UKGgGR8B9K9r56+nJaAdLYmgIR0AGnVAiV0LddX2UKGgGR8BEJldC3PRiaAdLUGgIR0AGzGkvboKVdX2UKGgGR8Bf7HR9gF5faAdLcWgIR0AHGpEQXhwVdX2UKGgGR8B8/ZVinYQKaAdLZWgIR0AHUBKcurZKdX2UKGgGR8B5muuIRAbAaAdLgGgIR0AHpaLXL/0edX2UKGgGR8CHp8g8KXv6aAdLk2gIR0AIAv38GcFydX2UKGgGR8B83902cawVaAdLe2gIR0AIWrQw9JSSdX2UKGgGR8CFFsFVT72taAdLsmgIR0AI3d69kBjndX2UKGgGR8CCcqgOBlMAaAdLhGgIR0AJNMmF8G9pdX2UKGgGR8CA7uWGATZhaAdLbGgIR0AJf5P/JeVtdX2UKGgGR8B7ckPnSv1UaAdLWmgIR0AJsMb3oLXudX2UKGgGR8B7No/dIoVmaAdLVWgIR0AJ9S4vvjOtdX2UKGgGR8CGh2I1LrX2aAdLm2gIR0AKVBfKISDidX2UKGgGR8CCVqw35vcaaAdLbGgIR0AKofhddE9ddX2UKGgGR8CD/2hdMTN/aAdLgWgIR0AK9fzBhx5tdX2UKGgGR8CAvJFKkEcLaAdLXWgIR0ALKRwIdELIdX2UKGgGR8BvK3s/pt78aAdLYWgIR0ALc6ij+JgtdX2UKGgGR8CF4sZof0VaaAdLm2gIR0AL2EkB0ZFYdX2UKGgGR8B4oapfhMrVaAdLXmgIR0AMILThHbypdX2UKGgGR8CCvntpmEoOaAdLhmgIR0AMehK15Sm7dX2UKGgGR8CGzL3dsSCfaAdLhmgIR0AM0Cih37k5dX2UKGgGR8CET+dKdxyXaAdLcGgIR0ANHd0q6OHWdX2UKGgGR8CFmfvH93r2aAdLgGgIR0ANcsH0K7ZndX2UKGgGR8BwWTyPMjeLaAdLd2gIR0ANs7r9l2/0dX2UKGgGR8B3L2kadc0MaAdLZGgIR0AN/1anrIHUdWUu"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 1570,
+    "_n_updates": 368,
     "observation_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
         ":serialized:": "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",
@@ -76,15 +76,15 @@
         "dtype": "int64",
         "_np_random": null
     },
-    "n_envs": 1,
-    "n_steps": 64,
-    "gamma": 0.99,
-    "gae_lambda": 0.95,
-    "ent_coef": 0.0,
+    "n_envs": 16,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 64,
-    "n_epochs": 10,
+    "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",
         ":serialized:": "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"

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8b60557806e1a0c5c9e7691441ab91e95509372e54bb5bb99ccd2c13e90c31a9
+oid sha256:bf65efc7a5b87358c5975106bd602b76ef5a7846deb01b014a393e74f46b98d4
 size 88375

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:69e607d5d5051dfe5726b633932d870a696dee1202246f2a2d87622b7fa507bf
+oid sha256:f8d8fa92475daba3e694fb3a65f66cb1c249b6c5e87d91f48138e4a23c6f7329
 size 43967

ppo-LunarLander-v2/system_info.txt

@@ -6,3 +6,4 @@
 - Numpy: 2.3.1
 - Cloudpickle: 3.1.1
 - Gymnasium: 0.29.1
+- OpenAI Gym: 0.26.2

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a81fb4f2581a2ee5fef315a25680f64346eab6570ca5b075af8d15864dcb6c6
-size 105187
+oid sha256:9273c38a384ce78a386dd97f03f41ddb91b35d05fa8c50f6f28779513a0263ec
+size 147188

results.json

@@ -1 +1 @@
-{"mean_reward": -1070.3353720092389, "std_reward": 1311.6980823102112, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-07-20T21:54:34.970975"}
+{"mean_reward": 262.23921137350516, "std_reward": 21.349143006703343, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-07-20T22:06:32.725818"}